Adequate physical activity can prevent or delay many chronic illnesses and enhance the quality of life. Despite these well-documented benefits, people adopt and maintain exercise at alarmingly low rates, and far too many people are entirely sedentary. This is due in part to our limited understanding of which interventions work to increase activity. Although many primary studies have tested interventions designed to increase activity, their findings remain unsynthesized, a condition that seriously impedes progress in both research and practice. The purpose of this project is to integrate scientific knowledge about interventions to increase physical activity in healthy people. The project addresses these specific aims: [unreadable] Determine the strength of the research base about interventions to increase physical activity. [unreadable] Specify the effect of interventions on physical activity and exercise behavior. [unreadable] Distinguish factors that moderate the effect of interventions to increase exercise and physical activity. Our research team has used the proposed methods in a published synthesis focused on aging subjects and in an in-progress NIH-funded meta-analysis of exercise interventions among chronically ill adults. An extremely extensive and rigorous literature search will avoid the bias caused by typical limited searches. Strategies include computerized searches, ancestry searches, registry and database searches, hand searches of selected journals, reviews of graduate projects, examination of conference/association abstracts, and contacts with senior authors on retrieved studies and principal investigators of NIH-funded studies. Independent data extractors will reliably code intervention, methodological and participant attributes that address the research aims. Analysis plans include: cf-index to standardize the magnitude of effect, both unweighted and sample size weighted calculations, analyses under both fixed and random effects models, potential control for methodological moderators in subsequent analysis, and homogeneity analysis (Qt, Qw) to detect intervention component effects. For example, the moderator analysis will reveal intervention characteristics (e.g. self-monitoring) are associated with larger increases in physical activity. Minority and gender differences in intervention effectiveness will be examined as well. Findings will improve public health by synthesizing diverse results so that interventions can be designed for effective programs that help people increase their physical activity to meet public health goals. The compelling importance and broad scope of this work make funding necessary to achieve these important aims.